linting issues

Author: Terji Petersen

pandas/tests/groupby/test_categorical.py

@@ -1543,9 +1543,7 @@ def test_read_only_category_no_sort():
     df = DataFrame(
         {"a": [1, 3, 5, 7], "b": Categorical([1, 1, 2, 2], categories=Index(cats))}
     )
-    expected = DataFrame(
-        data={"a": [2.0, 6.0]}, index=CategoricalIndex(cats, name="b")
-    )
+    expected = DataFrame(data={"a": [2.0, 6.0]}, index=CategoricalIndex(cats, name="b"))
     result = df.groupby("b", sort=False).mean()
     tm.assert_frame_equal(result, expected)
 

pandas/tests/groupby/test_groupby.py

@@ -1027,8 +1027,12 @@ def test_groupby_level_mapper(mframe):
     result0 = mframe.groupby(mapper0, level=0).sum()
     result1 = mframe.groupby(mapper1, level=1).sum()
 
-    mapped_level0 = np.array([mapper0.get(x) for x in deleveled["first"]], dtype=np.int64)
-    mapped_level1 = np.array([mapper1.get(x) for x in deleveled["second"]], dtype=np.int64)
+    mapped_level0 = np.array(
+        [mapper0.get(x) for x in deleveled["first"]], dtype=np.int64
+    )
+    mapped_level1 = np.array(
+        [mapper1.get(x) for x in deleveled["second"]], dtype=np.int64
+    )
     expected0 = mframe.groupby(mapped_level0).sum()
     expected1 = mframe.groupby(mapped_level1).sum()
     expected0.index.name, expected1.index.name = "first", "second"

pandas/tests/groupby/test_size.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pytest
 
-import pandas as pd
 from pandas import (
     DataFrame,
     Index,

pandas/tests/indexes/datetimelike_/test_drop_duplicates.py

@@ -35,8 +35,16 @@ def test_drop_duplicates_metadata(self, idx):
     @pytest.mark.parametrize(
         "keep, expected, index",
         [
-            ("first", np.concatenate(([False] * 10, [True] * 5)), np.arange(0, 10, dtype=np.int64)),
-            ("last", np.concatenate(([True] * 5, [False] * 10)), np.arange(5, 15, dtype=np.int64)),
+            (
+                "first",
+                np.concatenate(([False] * 10, [True] * 5)),
+                np.arange(0, 10, dtype=np.int64),
+            ),
+            (
+                "last",
+                np.concatenate(([True] * 5, [False] * 10)),
+                np.arange(5, 15, dtype=np.int64),
+            ),
             (
                 False,
                 np.concatenate(([True] * 5, [False] * 5, [True] * 5)),

pandas/tests/indexes/test_numpy_compat.py

@@ -86,7 +86,7 @@ def test_numpy_ufuncs_basic(index, func):
                 # TODO: find out when index.dtype is float16 vs float32 (platform issue)
                 assert result.dtype in ["float16", "float32"]
             elif index.dtype in ["float16"]:
-                assert result.dtype  == "float16"
+                assert result.dtype == "float16"
             elif index.dtype in ["int16", "uint16", "float32"]:
                 assert result.dtype == "float32"
             else:

pandas/tests/reshape/test_cut.py

@@ -88,7 +88,8 @@ def test_bins_from_interval_index_doc_example():
     ages = np.array([10, 15, 13, 12, 23, 25, 28, 59, 60])
     c = cut(ages, bins=[0, 18, 35, 70])
     expected = IntervalIndex.from_arrays(
-        left=np.array([0, 18, 35]), right=np.array([18, 35, 70]))
+        left=np.array([0, 18, 35]), right=np.array([18, 35, 70])
+    )
     tm.assert_index_equal(c.categories, expected)
 
     result = cut([25, 20, 50], bins=c.categories)